{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Step-by-step NMO correction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Devito is equally useful as a framework for other stencil computations in general; for example, computations where all array indices are affine functions of loop variables. The Devito compiler is also capable of generating\n",
    "arbitrarily nested, possibly irregular, loops. This key feature is needed to support many complex algorithms that are used in engineering and scientific practice, including applications from image processing, cellular automata, and machine-learning. This tutorial, a step-by-step NMO correction, is an example of it.  \n",
    "\n",
    "In reflection seismology, normal moveout (NMO) describes the effect that the distance between a seismic source and a receiver (the offset) has on the arrival time of a reflection in the form of an increase of time with offset. The relationship between arrival time and offset is hyperbolic. \n",
    "\n",
    "Based on the field geometry information, each individual trace is assigned to the midpoint between the shot and receiver locations associated with that trace. Those traces with the same midpoint location are grouped together, making up a common midpoint gather (CMP). \n",
    "\n",
    "Consider a reflection event on a CMP gather. The difference between the two-way time at a given offset and the two-way zero-offset time is called normal moveout (NMO). Reflection traveltimes must be corrected for NMO prior to summing the traces in the CMP gather along the offset axis. The normal moveout depends on velocity above the reflector, offset, two-way zero-offset time associated with the reflection event, dip of the reflector, the source-receiver azimuth with respect to the true-dip direction, and the degree of complexity of the near-surface and the medium above the reflector.\n",
    "\n",
    "<img src='./nmo-diagram.png' width=1000>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seismic modelling with devito"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before the NMO corretion we will describe a setup of seismic modelling with Devito in a simple 2D case. We will create a physical model of our domain and define a multiple source and an according set of receivers to model for the forward model. But first, we initialize some basic utilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sympy as sp\n",
    "\n",
    "from devito import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create a simple velocity model here by hand for demonstration purposes. This model essentially consists of three layers, each with a different velocity: 1.5km/s in the top layer, 2.5km/s in the middle layer and 4.5 km/s in the bottom layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `initdamp` run in 0.01 s\n",
      "Operator `padfunc` run in 0.01 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NBVAL_IGNORE_OUTPUT\n",
    "from examples.seismic import Model, plot_velocity\n",
    "\n",
    "shape = (301, 501)  # Number of grid point (nx, ny, nz)\n",
    "spacing = (10., 10)  # Grid spacing in m. The domain size is now 3km by 5km\n",
    "origin = (0., 0)  # What is the location of the top left corner.\n",
    "\n",
    "# Define a velocity profile. The velocity is in km/s\n",
    "v = np.empty(shape, dtype=np.float32)\n",
    "v[:,:100] = 1.5\n",
    "v[:,100:350] = 2.5\n",
    "v[:,350:] = 4.5\n",
    "\n",
    "# With the velocity and model size defined, we can create the seismic model that\n",
    "# encapsulates these properties. We also define the size of the absorbing layer as 10 grid points\n",
    "model = Model(vp=v, origin=origin, shape=shape, spacing=spacing, space_order=4, nbl=40)\n",
    "\n",
    "plot_velocity(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we define the positioning and the wave signal of our source, as well as the location of our receivers. To generate the wavelet for our sources we require the discretized values of time that we are going to use to model a multiple \"shot\", which depends on the grid spacing used in our model. We will use one source and eleven receivers. The source is located in the position (550, 20). The receivers start at (550, 20) with an even horizontal spacing of 100m at consistent depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from examples.seismic import TimeAxis\n",
    "\n",
    "t0 = 0.  # Simulation starts a t=0\n",
    "tn = 2400.  # Simulation last 2.4 second (2400 ms)\n",
    "dt = model.critical_dt  # Time step from model grid spacing\n",
    "\n",
    "time_range = TimeAxis(start=t0, stop=tn, step=dt)\n",
    "\n",
    "nrcv = 250 # Number of Receivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NBVAL_IGNORE_OUTPUT\n",
    "from examples.seismic import RickerSource\n",
    "\n",
    "f0 = 0.010  # Source peak frequency is 10Hz (0.010 kHz)\n",
    "src = RickerSource(name='src', grid=model.grid, f0=f0,\n",
    "                   npoint=1, time_range=time_range)\n",
    "\n",
    "# Define the wavefield with the size of the model and the time dimension\n",
    "u = TimeFunction(name=\"u\", grid=model.grid, time_order=2, space_order=4)\n",
    "\n",
    "# We can now write the PDE\n",
    "pde = model.m * u.dt2 - u.laplace + model.damp * u.dt\n",
    "stencil = Eq(u.forward, solve(pde, u.forward))\n",
    "\n",
    "src.coordinates.data[:, 0] = 400 # Source coordinates\n",
    "src.coordinates.data[:, -1] = 20.  # Depth is 20m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `Kernel` run in 1.25 s\n"
     ]
    }
   ],
   "source": [
    "#NBVAL_IGNORE_OUTPUT\n",
    "from examples.seismic import Receiver\n",
    "\n",
    "rec = Receiver(name='rec', grid=model.grid, npoint=nrcv, time_range=time_range)\n",
    "rec.coordinates.data[:,0] = np.linspace(src.coordinates.data[0, 0], model.domain_size[0], num=nrcv)\n",
    "rec.coordinates.data[:,-1] = 20.  # Depth is 20m\n",
    "\n",
    "# Finally we define the source injection and receiver read function to generate the corresponding code\n",
    "src_term = src.inject(field=u.forward, expr=src * dt**2 / model.m)\n",
    "\n",
    "# Create interpolation expression for receivers\n",
    "rec_term = rec.interpolate(expr=u.forward)\n",
    "\n",
    "op = Operator([stencil] + src_term + rec_term, subs=model.spacing_map)\n",
    "op(time=time_range.num-1, dt=model.critical_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How we are modelling a horizontal layers, we will group this traces and made a NMO correction using this set traces. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "offset = []\n",
    "data = []\n",
    "for i, coord in enumerate(rec.coordinates.data):\n",
    "    off = (src.coordinates.data[0, 0] - coord[0])\n",
    "    offset.append(off)\n",
    "    data.append(rec.data[:,i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auxiliary function for plotting traces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NBVAL_IGNORE_OUTPUT\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "mpl.rc('font', size=16)\n",
    "mpl.rc('figure', figsize=(8, 6))\n",
    "\n",
    "def plot_traces(rec, xb, xe, t0, tn, colorbar=True):\n",
    "    scale = np.max(rec)/100    \n",
    "    extent = [xb, xe, 1e-3*tn, t0]\n",
    "    plot = plt.imshow(rec, cmap=cm.gray, vmin=-scale, vmax=scale, extent=extent)\n",
    "    plt.xlabel('X position (km)')\n",
    "    plt.ylabel('Time (s)')\n",
    "\n",
    "    # Create aligned colorbar on the right\n",
    "    if colorbar:\n",
    "        ax = plt.gca()\n",
    "        divider = make_axes_locatable(ax)\n",
    "        cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "        plt.colorbar(plot, cax=cax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Common Midpoint Gather"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we have a dataset composed of the receivers. \"If our model wasn't purely horizontal, we would have to sort these traces by common midpoints prior to NMO correction.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_traces(np.transpose(data), rec.coordinates.data[0][0]/1000, rec.coordinates.data[nrcv-1][0]/1000, t0, tn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# NMO Correction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can correct the measured traveltime of a reflected wave $t$ at a given offset $x$ to obtain the traveltime at normal incidence $t_0$ by applying the following equation:\n",
    "\n",
    "\\begin{equation*}\n",
    "t = \\sqrt{t_0^2 + \\frac{x^2}{V_{nmo}^2}} \n",
    "\\end{equation*}\n",
    "\n",
    "in which $V_{nmo}$ is the NMO velocity. This equation results from the Pythagorean theorem, and is only valid for horizontal reflectors. There are variants of this equation with different degrees of accuracy, but we'll use this one for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the NMO Correction we use a grid of size samples x traces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ns = time_range.num # Number of samples in each trace\n",
    "grid = Grid(shape=(ns, nrcv)) # Construction of grid with samples X traces dimension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we will use a constant velocity guide. The guide will be arranged in a SparseFunction with the number of points equal to number of samples in the traces. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vnmo = 1500\n",
    "vguide = SparseFunction(name='v', grid=grid, npoint=ns)\n",
    "vguide.data[:] = vnmo  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The computed offset for each trace will be arraged in another SparseFunction with number of points equal to number of traces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "off = SparseFunction(name='off', grid=grid, npoint=nrcv)\n",
    "off.data[:] = offset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous modelled traces will be arranged in a SparseFunction with the same dimensions as the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "amps = SparseFunction(name='amps', grid=grid, npoint=ns*nrcv, dimensions=grid.dimensions, shape=grid.shape)\n",
    "amps.data[:] = np.transpose(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we define SparseFunctions with the same dimensions as the grid, describing the NMO traveltime equation. The $t_0$ SparseFunction isn't offset dependent, so the number of points is equal to the number of samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample, trace = grid.dimensions\n",
    "\n",
    "t_0 = SparseFunction(name='t0', grid=grid, npoint=ns, dimensions=[sample], shape=[grid.shape[0]])\n",
    "tt = SparseFunction(name='tt', grid=grid, npoint=ns*nrcv, dimensions=grid.dimensions, shape=grid.shape)\n",
    "snmo = SparseFunction(name='snmo', grid=grid, npoint=ns*nrcv, dimensions=grid.dimensions, shape=grid.shape)\n",
    "s = SparseFunction(name='s', grid=grid, dtype=np.intc, npoint=ns*nrcv, dimensions=grid.dimensions, \n",
    "                   shape=grid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Equation relates traveltimes: the one we can measure ($t_0$) and the one we want to know (t). But the data in our CMP gather are actually a matrix of amplitudes measured as a function of time ($t_0$) and offset. Our NMO-corrected gather will also be a matrix of amplitudes as a function of time (t) and offset. So what we really have to do is transform one matrix of amplitudes into the other.\n",
    "\n",
    "With Equations we describe the NMO traveltime equation, and use the Operator to compute the traveltime and the samples for each trace. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `Kernel` run in 0.01 s\n"
     ]
    }
   ],
   "source": [
    "#NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "dtms = model.critical_dt/1000 # Time discretization in ms\n",
    "E1 = Eq(t_0, sample*dtms)\n",
    "E2 = Eq(tt, sp.sqrt(t_0**2 + (off[trace]**2)/(vguide[sample]**2) ))\n",
    "E3 = Eq(s, sp.floor(tt/dtms))\n",
    "op1 = Operator([E1, E2, E3])\n",
    "op1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the computed samples, we remove all that are out of the samples range, and shift the amplitude for the correct sample. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `Kernel` run in 0.01 s\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "s.data[s.data >= time_range.num] = 0\n",
    "E4 = Eq(snmo, amps[s[sample, trace], trace])\n",
    "\n",
    "op2 = Operator([E4])\n",
    "op2()\n",
    "\n",
    "stack = snmo.data.sum(axis=1) # We can stack traces and create a ZO section!!! \n",
    "\n",
    "plot_traces(snmo.data, rec.coordinates.data[0][0]/1000, rec.coordinates.data[nrcv-1][0]/1000, t0, tn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References:\n",
    "    \n",
    "    https://library.seg.org/doi/full/10.1190/tle36020179.1\n",
    "    https://wiki.seg.org/wiki/Normal_moveout\n",
    "    https://en.wikipedia.org/wiki/Normal_moveout"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
